{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "a04b910d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    age  height  weight gender\n",
      "0    21     163      60      M\n",
      "1    22     164      56      M\n",
      "2    21     170      50      M\n",
      "3    23     168      56      M\n",
      "4    21     169      60      M\n",
      "..  ...     ...     ...    ...\n",
      "95   24     192      73      F\n",
      "96   25     187      74      F\n",
      "97   20     178      65      F\n",
      "98   23     172      76      F\n",
      "99   25     173      78      F\n",
      "\n",
      "[100 rows x 4 columns]\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "# 定义列名列表（变量名修正为names）\n",
    "names = ['age', 'height', 'weight', 'gender']\n",
    "# 使用绝对路径读取文件（拼接完整路径）\n",
    "file_path = '/Users/somi/machine/006/item6/gender-data-y.txt'\n",
    "dataset = pd.read_csv(file_path, delimiter=',', names=names)\n",
    "print(dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "3d5318c2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    age  height  weight gender  label\n",
      "0    21   163.0    60.0      M      1\n",
      "1    22   164.0    56.0      M      1\n",
      "2    21   170.0    50.0      M      1\n",
      "3    23   168.0    56.0      M      1\n",
      "4    21   169.0    60.0      M      1\n",
      "..  ...     ...     ...    ...    ...\n",
      "95   24   192.0    73.0      F      0\n",
      "96   25   187.0    74.0      F      0\n",
      "97   20   178.0    65.0      F      0\n",
      "98   23   172.0    76.0      F      0\n",
      "99   25   173.0    78.0      F      0\n",
      "\n",
      "[100 rows x 5 columns]\n"
     ]
    }
   ],
   "source": [
    "from sklearn import preprocessing\n",
    "# 数据类型转换（转换成浮点）\n",
    "\n",
    "dataset['height'] = dataset['height'].astype(float)\n",
    "dataset['weight'] = dataset['weight'].astype(float)\n",
    "# 对性别进行标签编码\n",
    "\n",
    "le = preprocessing.LabelEncoder()\n",
    "dataset['label'] = le.fit_transform(dataset['gender'])\n",
    "print(dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "849b9b7b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.font_manager import FontProperties\n",
    "\n",
    "data = dataset.iloc[range(0, 100), range(1, 3)].values\n",
    "target = dataset.iloc[range(0, 100), range(4,5)].values.reshape(1,100)[0]\n",
    "\n",
    "#绘制散点图\n",
    "plt.scatter(data[target==0,0],data[target==0,1],s=60,c='r',marker='o')   #绘制标签为0的样本点\n",
    "plt.scatter(data[target==1,0],data[target==1,1],s=60,c='g',marker='^')\t #绘制标签为1的样本点\n",
    "\n",
    "# 设置坐标轴\n",
    "plt.rcParams[\"font.family\"] = [\"sans-serif\"]  # 基础字体族（可保持默认）\n",
    "plt.rcParams[\"font.sans-serif\"] = [FontProperties(fname=\"/Users/somi/machine/Hiragino Sans GB.ttc\").get_name()]\n",
    "plt.rcParams['axes.unicode_minus'] = False\n",
    "plt.xlabel('身高（cm）')\n",
    "plt.ylabel('体重（kg）')\n",
    "plt.title('身高与体重分布图')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "6147c59f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.19999999999999996, 0.19999999999999996, 0.19999999999999996, 0.16666666666666663, 0.1333333333333333, 0.1333333333333333, 0.16666666666666663, 0.16666666666666663, 0.16666666666666663, 0.16666666666666663, 0.16666666666666663, 0.16666666666666663, 0.16666666666666663, 0.16666666666666663]\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.metrics import accuracy_score\n",
    "from matplotlib.font_manager import FontProperties\n",
    "\n",
    "x, y = data, target\n",
    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=30, random_state=0)\n",
    "\n",
    "depth = np.arange(1, 15)\n",
    "err_list = []\n",
    "for d in depth:\n",
    "    model=DecisionTreeClassifier(criterion='entropy',max_depth=d)\n",
    "    model.fit(x_train, y_train)\n",
    "    pred = model.predict(x_test)\n",
    "    ac = accuracy_score(y_test, pred)\n",
    "    err = 1 - ac\n",
    "    err_list.append(err)\n",
    "print(err_list)\n",
    "\n",
    "\n",
    "# 绘制深度值与误差率的图像\n",
    "plt.plot(depth, err_list, 'ro-')\n",
    "plt.rcParams[\"font.family\"] = [\"sans-serif\"]  # 基础字体族（可保持默认）\n",
    "plt.rcParams[\"font.sans-serif\"] = [FontProperties(fname=\"/Users/somi/machine/Hiragino Sans GB.ttc\").get_name()]  # 用来正常显示中文标签\n",
    "plt.xlabel('决策树深度值')\n",
    "plt.ylabel('误差率')\n",
    "plt.title('决策树深度值与误差率关系图')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "6bcb219e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "评估报告：\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.73      0.85        15\n",
      "           1       0.79      1.00      0.88        15\n",
      "\n",
      "    accuracy                           0.87        30\n",
      "   macro avg       0.89      0.87      0.86        30\n",
      "weighted avg       0.89      0.87      0.86        30\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "\n",
    "# 深度值：5\n",
    "model = DecisionTreeClassifier(criterion='entropy', max_depth=5)\n",
    "model.fit(x_train, y_train)\n",
    "\n",
    "# 对模型进行评估，并输出评估报告\n",
    "pred = model.predict(x_test)\n",
    "report = classification_report(y_test, pred)\n",
    "print('评估报告：')\n",
    "print(report)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "ad6b8bd0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib.colors import ListedColormap\n",
    "from matplotlib.font_manager import FontProperties\n",
    "\n",
    "# 绘制决策边界函数\n",
    "N, M = 500, 500  # 横纵各采样多少个点\n",
    "t1 = np.linspace(140, 195, N)\n",
    "t2 = np.linspace(30, 90, M)\n",
    "x1, x2 = np.meshgrid(t1, t2)  # 生成网格采样点\n",
    "x_new = np.stack((x1.flat, x2.flat), axis=1)  # 测试点\n",
    "y_new = model.predict(x_new)  # 预测分类结果\n",
    "y_hat = y_new.reshape(x1.shape)  # 使之与输入的形状相同\n",
    "iris_cmap = ListedColormap(['#FF8080', '#90EE90'])\n",
    "plt.pcolormesh(x1, x2, y_hat, cmap=iris_cmap)  # 预测值的显示\n",
    "plt.scatter(x[y==0,0],x[y==0,1],s=60,c='r',marker='o')\t\t\t                                #绘制标签为0的样本点\n",
    "plt.scatter(x[y==1,0],x[y==1,1],s=60,c='g',marker='^')\n",
    "plt.rcParams[\"font.family\"] = [\"sans-serif\"]  # 基础字体族（可保持默认）\n",
    "plt.rcParams[\"font.sans-serif\"] = [FontProperties(fname=\"/Users/somi/machine/Hiragino Sans GB.ttc\").get_name()]  # 用来正常显示中文标签\n",
    "plt.xlabel('身高（cm）')\n",
    "plt.ylabel('体重（kg）')\n",
    "plt.title('决策树分类结果展示')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "530aae62",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "ml-course",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
